Professional Overview
AI/ML Engineer with hands-on experience in building intelligent systems using Machine Learning, Deep Learning, Computer Vision, and Large Language Models (LLMs). Skilled in developing RAG pipelines, agentic AI workflows, real-time voice-enabled AI applications, and production-ready AI services using Python, FastAPI, and cloud technologies. Experien
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Designed and delivered agentic AI systems with LLM reasoning
- Built retrieval-augmented generation pipelines for enterprise use
- Developed realtime voice AI workflows using GPT Realtime API
- Created FastAPI services for AI inference and streaming
- Deployed AI applications on AWS using Docker containers
Key Achievements / KPIs
- Delivered production-ready AI systems serving real business needs
- Reduced response latency through optimized streaming pipelines
- Implemented secure cloud deployment and authentication workflows
- Improved AI answer quality using RAG and reasoning techniques
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built AI-powered desktop applications using Electron and FastAPI
- Developed computer vision and deep learning solutions
- Trained and evaluated YOLO and CNN models
- Created document-processing and image-analysis pipelines
- Integrated AI inference services into production applications
Key Achievements / KPIs
- Improved workflow automation through AI-powered desktop tools
- Increased model accuracy through dataset optimization techniques
- Reduced inference time using optimized image-processing pipelines
- Successfully deployed multiple AI solutions into production
Education
Field of Study: Computer Science
Areas of Expertise
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
Data Science & Analytics
Extract business insights from raw data using statistical modelling, predictive analytics, A/B testing, and interactive dashboards.
MLOps & AI Infrastructure
Architect scalable ML infrastructure — CI/CD for ML, model versioning, experiment tracking, monitoring, and cloud deployment on AWS, GCP, or Azure.
AI Product & Strategy
Translate AI capabilities into product roadmaps, define success metrics, and drive adoption across engineering and business teams.
